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Author(s):  
Daniel Braun ◽  
Ronny Müller

Abstract Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the positions allows one to represent the quantum mechanical state of $\nb$ qubits by $2(\nb+1)$ classical stochastic bits. Based on this, we demonstrate many-particle interference and representation of pure entangled quantum states via derivatives of probability distributions and find the universal set of stochastic maps that correspond to the quantum gates in a universal gate set. We prove that the propagation via the stochastic map built from those universal stochastic maps reproduces up to a prefactor exactly the evolution of the quantum mechanical state with the corresponding quantum algorithm, leading to an automated translation of a quantum algorithm to a stochastic classical algorithm. We implement several well-known quantum algorithms, analyse the scaling of the needed number of realizations with the number of qubits, and highlight the role of destructive interference for the cost of the emulation.


ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 97
Author(s):  
Emanuele Buchicchio ◽  
Francesco Santoni ◽  
Alessio De Angelis ◽  
Antonio Moschitta ◽  
Paolo Carbone

<p class="Abstract"><span lang="EN-US">Gesture recognition is a fundamental step to enable efficient communication for the deaf through the automated translation of sign language. This work proposes the usage of a high-precision magnetic positioning system for 3D positioning and orientation tracking of the fingers and hands palm. The gesture is reconstructed by the MagIK (magnetic and inverse kinematics) method and then processed by a deep learning gesture classification model trained to recognize the gestures associated with the sign language alphabet. Results confirm the limits of vision-based systems and show that the proposed method based on hand skeleton reconstruction has good generalization properties. The proposed system, which combines sensor-based gesture acquisition and deep learning techniques for gesture recognition, provides a 100% classification accuracy, signer independent, after a few hours of training using transfer learning technique on well-known ResNet CNN architecture. The proposed classification model training method can be applied to other sensor-based gesture tracking systems and other applications, regardless of the specific data acquisition technology.</span></p>


2021 ◽  
Vol 2 (4) ◽  
pp. 23-30
Author(s):  
Chenliang Zhou

This paper has adopted a quantitative approach to carry out a linguistic study, within the theoretical framework of dependency grammar. Translation is a process where source language and target language interact with each other. The present study aims at exploring the feasibility of mean dependency distance as a metric for automated translation quality assessment. The current research hypothesized that different levels of translation are significantly different in the aspect of mean dependency distance. Data of this study were based on the written translation in Parallel Corpus of Chinese EFL Learners which was composed of translations from Chinese EFL learners in various topic. The translations were human-scored to determine the levels of translation, according to which the translations were categorized. Our results indicated that: (1) senior students perform better in translation than junior students, and mean dependency distance of translations from senior group is significantly shorter than the junior; (2) high quality translations yield shorter mean dependency distance than the low quality translations; (3) mean dependency distance of translations is moderately correlated with the human score. The resultant implication suggests the potential for mean dependency distance in differentiating translations of different quality.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Beatriz Carvalho Henriques ◽  
Avery Buchner ◽  
Xiuying Hu ◽  
Yabing Wang ◽  
Vasyl Yavorskyy ◽  
...  

AbstractMany antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.


2021 ◽  
Author(s):  
Jan Buts

Abstract This article argues that researchers in Translation Studies may proactively aim to understand the consequences of an envisaged merger between targeted advertising and automated translation. Functional translation software is widely available online, and several platforms now perform instant translation, sometimes without asking the user whether this is required. Indeed, the user’s main language is known to various applications, which keep track of this information along with other settings and preferences. Data tracking is commonly used to produce targeted advertising: people receive commercial information about products they are likely to be interested in. If text can instantly be altered according to a user’s linguistic preferences, it can also be altered according to aesthetic, commercial, or political preferences. The article discusses theoretical and ideological aspects of the sociotechnical evolution towards the production and consumption of personalised content, highlighting the role translation may come to play.


Author(s):  
Rehab A. A. Mohameed ◽  
Ruba M. S. Naji ◽  
Afnan M. A. Ahmeed ◽  
Dina A. A. Saeed ◽  
Mogeeb A. A. Mosleh

2021 ◽  
Vol 87 (7) ◽  
pp. 76-84
Author(s):  
N. I. Mulatov ◽  
A. S. Mokhov ◽  
V. О. Tolcheev

We report on solving the problem of forming a Russian-language text collection (dataset) consisting of bibliographic descriptions of scientific articles for training classifiers. Various approaches to creating such collections are considered. The expediency of using expert estimates for assigning class labels is assessed. The known datasets are analyzed, the requirements for the generated text array are formulated, and the choice of the subject area (Computer Science) is justified. We propose a technology of forming collection in conditions of the shortage of Russian-language articles. To do this we use automated translation of publications (bibliographic descriptions) from available English-language electronic libraries (ACM digital library, IEEE Xplore digital library, CiteSeerX) with additional expert quality control of the translation. The bibliographic collection thus formed was studied using methods of clustering (Latent Semantic Analysis) and visualization (Principal Component Analysis). Training and test samples were compiled and «standard» classifiers (K-Nearest Neighbor Method, Logistic Regression, Random Forest) were used. Then we calculated standard quality measures (accuracy, precision, recall). The rigid and soft classification were carried out. For rigid and soft classification all calculated measures (for the studied classifiers) ranged within [0.79; 0.87], and [0.91; 0.95], respectively. The experiments showed almost identical results for Russian and English bibliographic descriptions (the difference did not exceed 2%). The proposed method of forming text collections reduces the complexity of the labeling process compared to the expert approach, solves the problem of the lack of Russian-language documents, allows formation of sufficiently large balanced bibliographic datasets for training and testing classifiers.


2021 ◽  
Vol 11 (12) ◽  
pp. 5594
Author(s):  
Matteo Rinalduzzi ◽  
Alessio De Angelis ◽  
Francesco Santoni ◽  
Emanuele Buchicchio ◽  
Antonio Moschitta ◽  
...  

Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.


Author(s):  
John Derrick ◽  
Simon Doherty ◽  
Brijesh Dongol ◽  
Gerhard Schellhorn ◽  
Heike Wehrheim

AbstractNon-volatile memory (NVM), aka persistent memory, is a new memory paradigm that preserves its contents even after power loss. The expected ubiquity of NVM has stimulated interest in the design of persistent concurrent data structures, together with associated notions of correctness. In this paper, we present a formal proof technique for durable linearizability, which is a correctness criterion that extends linearizability to handle crashes and recovery in the context ofNVM.Our proofs are based on refinement of Input/Output automata (IOA) representations of concurrent data structures. To this end, we develop a generic procedure for transforming any standard sequential data structure into a durable specification and prove that this transformation is both sound and complete. Since the durable specification only exhibits durably linearizable behaviours, it serves as the abstract specification in our refinement proof. We exemplify our technique on a recently proposed persistentmemory queue that builds on Michael and Scott’s lock-free queue. To support the proofs, we describe an automated translation procedure from code to IOA and a thread-local proof technique for verifying correctness of invariants.


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